Methods and systems for pharmacy modeling

ABSTRACT

Methods and systems for pharmacy modeling are described. The risk adjusted pharmacy predictive model is created from member data, claims data, and population data. This model can be used to compare the actual pharmacy performance to an expected actual pharmacy performance value, which can be used to identify pharmacies at risk or not performing to an acceptable level. The model can be used for adherence and generic drug utilization ratings of pharmacies. The pharmacy can be judged on a therapy class by therapy class basis with factors that reflect the demographic, socio-economic, location, benefits attributes, etc. that actually affect the performance of the pharmacy and may assist in determining the quality of care by a pharmacy.

CROSS-REFERENCE TO A RELATED APPLICATION

This application claims the benefit of U.S. Provisional PatentApplication No. 61/934,133, filed on 31 Jan. 2014, titled “Methods andSystems for Risk Adjustment Pharmacy Modeling,” the entire disclosure ofwhich is incorporated herein by reference.

FIELD

The field relates to predictive modeling, and more specifically topredictive modeling using pharmacy data including drug claims data andnon-claims pharmacy data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example system, according to an exampleembodiment;

FIG. 2 is a block diagram of an example user device that may be deployedwithin the system of FIG. 1, according to an example embodiment;

FIG. 3 is a block diagram of an example benefit manager device that maybe deployed within the system of FIG. 1, according to an exampleembodiment;

FIG. 4 is a process flow illustrating a method of generating apredictive model, according to an example embodiment;

FIG. 5 is an example display, according to an example embodiment; and

FIG. 6 is a block diagram of a machine in the example form of a computersystem within which a set of instructions for causing the machine toperform any one or more of the methodologies discussed herein may beexecuted or stored.

DETAILED DESCRIPTION

Example methods and systems for pharmacy modeling are described. Themodels can be used to determine quality of care provided by the pharmacyor value provided by the pharmacy, e.g., clinical quality of a retailpharmacy. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of example embodiments. It will be evident, however, toone of ordinary skill in the art that embodiments of the invention maybe practiced without these specific details.

The Centers for Medicare and Medicaid Services (“CMS”) has established aquality rating system to help consumers, their families, and caregiverscompare nursing homes more easily and to help identify areas about whicha consumer may want to ask questions. CMS has extended the star ratingsystem to drug plans. Measuring quality in the pharmacy network forhealth plans may be performed to, among other things, improve starratings.

Pharmacy quality measures can be explicitly defined and measured withrelative accuracy using pharmacy claims data. The methods and systemsmay enable measures that improve accuracy. For example, the improvedmeasures adjust for factors that are beyond the control of thepharmacies such as demographics of patients serviced, plan designfactors, prior authorizations, and the like. The methods and systemsprovide a methodology to empirically adjust the pharmacy quality measurethat eliminates the influence of factors that are beyond the control ofindividual pharmacies. That is, the methods and systems take intoaccount indirect factors that are beyond the control of the pharmacy tobetter indicate the pharmacy quality measure. Factors can be datarelated to a pharmacy, e.g., member data, claims data, and populationdata.

In some embodiments, a pharmacy model may be used to determine astandard to which pharmacies can be held for the adherence, or improvingthe adherence, for an associated pharmacy patient population. Otherstandards that can be measured using the present disclosure includegeneric dispensing ratio. Each of these models can be determined using aheuristic system that begins with numerous factors available to thesystem. These factors relate to the pharmacy but may be directed to thedemographic and socioeconomic attributes of the members (e.g., patientsbeing served by the pharmacy), pharmacy benefit design attributes,location attributes, and therapy class. The methods and systems reducethese factors to only those factors that influence the actualperformance of the pharmacy.

In some embodiments, a pharmacy model may be developed using dimensionreduction techniques and/or a heuristic process. While a large number ofinitial variables may be considered (e.g., around 300 variables), alesser number may be selected and then a variable selection technique(e.g., PROC GLMSELECT) may be used to further select appropriatevariables to include in the predictive model. That is, variables thatare predictive of pharmacy performance are distilled from the threehundred plus variables to a number of variables that can be moreefficiently processed. The variable reduction process can be performedin multiple steps, e.g., two, three or four steps, with variables beingremoved at each stage. The stages may each use a different technique todetermine if a variable remains in the model or is removed from themodel.

In some embodiments, the pharmacy model may compute a percentage ofpatients of a particular pharmacy that are expected to be adherent. Thispercentage may be compared against the actual percentage of patients ofthe pharmacy that are adherent.

In some embodiments, the pharmacy model may compute an expected genericdispensing ratio of a particular pharmacy. This calculated, expectedratio may then be compared against the actual generic dispensing ratioof the pharmacy.

FIG. 1 is a block diagram of an example system 100, according to anexample embodiment. The system 100 includes a user device 102 incommunication with a benefit manager device 106 and/or a pharmacy device108 over a network 104.

The user device 102 is used by a device operator. The user device 102may be a stand-alone device that solely provides at least some of thefunctionality to enable pharmacy modeling.

Examples of the devices 102, 106, 108 include a processing circuitry, aset-top box (STB), a receiver card, a mobile telephone, a personaldigital assistant (PDA), a display device, a portable gaming unit, and acomputing system; however other devices for modelling and using a modelmay also be used. For example, the user device 102 may include a mobileelectronic device, such an IPHONE or IPAD device by Apple, Inc., mobileelectronic devices powered by ANDROID by Google, Inc., and a BLACKBERRYdevice by Research In Motion Limited. The user device 102 also includeother computing devices, such as desktop computing devices, notebookcomputing devices, netbook computing devices, gaming devices, and thelike. Other types of electronic devices may also be used. Each of thesedevices includes circuitry that exemplifies a dedicated computing devicewhen performing any of the processes described herein.

The network 104 by which one or more than one of the devices 102, 106,108 may include, by way of example, Mobile Communications (GSM) network,a code division multiple access (CDMA) network, 3rd GenerationPartnership Project (3GPP), an Internet Protocol (IP) network, aWireless Application Protocol (WAP) network, a WiFi network, or an IEEE802.11 standards network, as well as various combinations thereof. Thenetwork 104 may also include optical communications. Wirelesscommunications include a broadcast signal embodying information throughthe air or a directed signal also through the air. Other conventionaland/or later developed wired and wireless networks may also be used. Insome embodiments, the network 104 may include proprietary networkcommunication technologies such as secure socket layers (SSL)technology, technology found in a prescribing network (e.g., theelectronic prescribing network operated by Surescripts of Arlington,Va.), and the like.

The benefit manager device 106 is a device operated by an entity atleast partially responsible for the management of a drug benefitprogram. While the entity operating the benefit manager device 106 istypically a pharmacy benefits manager (“PBM”), other entities mayoperate the benefit manager device 106 either on behalf of themselves,the PBM, or another entity.

Some of the operations of the PBM that operates the benefit managerdevice 106 may include the following. A member (or a person on behalf ofthe member) attempts to obtain a prescription drug at a retail pharmacylocation where the member can obtain drugs in a physical store from apharmacist or pharmacist technician, or in some instances through mailorder drug delivery from a mail order pharmacy location. A member is apatient that is entitled to a pharmacy benefit.

The member may have a co-pay for the prescription drug that reflects anamount of money that the member is responsible to pay the pharmacy forthe prescription drug. The money paid by the member to the pharmacy maycome from the personal funds of the member, a health savings account(HSA) of the member or the member's family, a health reimbursementarrangement (HRA) of the member or the member's family, a flexiblespending accounts (FSA) of the member or the member's family, or thelike. An employer of the member may directly or indirectly fund orreimburse the member or an account of the member for the co-pay.

In conjunction with receiving the co-pay (if any) from the member anddispensing the prescription drug to the member, the pharmacy submits aclaim to the PBM for the prescription drug. The PBM may perform certainadjudication functions including verifying the eligibility of themember, reviewing the formulary to determine appropriate co-pay,coinsurance, and deductible for the prescription drug, and performing adrug utilization review (DUR) on the member. The PBM then adjudicatesthe claim associated with the prescription drug and provides a responseto the pharmacy following performance of the aforementioned functions.As part of the adjudication, the client (or the PBM on behalf of theclient) ultimately reimburses the pharmacy for filling the prescriptiondrug when the prescription drug was successfully adjudicated. Theaforementioned adjudication functions generally occur before the co-payis received and the prescription drug dispensed. However, the operationsmay occur simultaneously, substantially simultaneously, or in adifferent order. In addition, more or less adjudication functions may beperformed as part of the adjudication process. Certain adjudications,payments and business relationship between the pharmacy and the PBM maydepend in part on the calculated quality of the pharmacy using themodel.

The pharmacy device 102 may include pharmacy hardware and/or software ofto enable the pharmacy (e.g., a mail order pharmacy and/or or a retailpharmacy) to fulfill prescription drug orders. The pharmacy device 102may be operated in an automated manner as directed by an operator (e.g.,a pharmacist or pharmacist technician), manually (e.g., by a pharmacistor pharmacist technician), or otherwise. Examples of pharmacy operationsthat may be performed by pharmacy device 102 include filling aprescription after removing pharmaceuticals from inventory, labeling acontainer with prescription information, filling a container, blisterpack, or other packaging with the pharmaceutical, verifying the type andquantity of the pharmaceutical in the container with that which isprinted on the label, capping or otherwise closing the packaging,preparing the packaging for shipment or other delivery to a patientassociated with the prescription, and the like.

In some embodiments, the pharmacy device 108 may be a device associatedwith a retail pharmacy location (e.g., an independent pharmacy or apharmacy location of a local or national chain such as WALGREENS, DUANEREED or CVS), a grocery store with a retail pharmacy (e.g., anindependent grocery store or a grocery store location of a local ornational chain such as ALDI, KROGERS, or SCHNUCKS) or a general salesstore with a retail pharmacy (e.g., an independent general sales storeor a store location of a local or national chain such as WALMART orTARGET) or other type of pharmacy location at which a member attempts toobtain a prescription. In some embodiments, the pharmacy device 108 maybe utilized to submit the claim to the PBM for adjudication.Additionally, in some embodiments, the pharmacy device 108 may enableinformation exchange between the pharmacy and the PBM, for example, toallow the sharing of member information such as drug history, and thelike, that may allow the pharmacy to better service a member (e.g., byproviding more informed therapy consultation and drug interactioninformation, etc.). This member information may also be used indetermining a pharmacy model or determining pharmacy quality using thepharmacy model.

In some embodiments, the pharmacy device 108 may be associated with amail order pharmacy. The mail order pharmacy may fill or refill theprescription, and may deliver the prescription drug to the member via aparcel service in accordance with an anticipated need, such as atime-wise schedule, or the like. As such, the member may not need tovisit the retail pharmacy store in person to have the prescriptionrefilled and/or to pick up the refilled prescription. In addition to theconvenience of receiving the refills of the prescription directly to themember's home or other designated location of delivery, the cost of theprescription drugs purchased through a mail order delivery pharmacy maybe less than the cost of the same prescription drugs purchased from aretail pharmacy. The lower costs available through the mail orderpharmacy may be the result, for example, of economies available to themail order pharmacy that may be at least partially passed along to themember as well as the savings realized by the client. The lower costsavailable through the mail order pharmacy may be the result of a lowerco-pay required by the member according to a health care plan, underwhich the member may receive the prescription drugs. The pharmacy device108 may communicate with the benefit manager device 106 in a similarmanner as described above. This information associated with the mailorder pharmacy may also be used in determining a pharmacy model ordetermining pharmacy quality using the pharmacy model.

The user device 102 may be in a client-server relationship with thedevices 106, 108 a peer-to-peer relationship with the devices 106, 108,and/or in a different type of relationship with the devices 106, 108.

The benefit manager device 106 may be in communication directly (e.g.,through local storage) and/or through the network 104 (e.g., in a cloudconfiguration or software as a service) with a database 110. Thedatabase 110 may be deployed on the user device 102, the benefit managerdevice 106, both the user device 102 and the benefit manager device 106,partially on the user device 102 and partially on the benefit managerdevice 106, on a separate device, or may otherwise be deployed. Thedatabase 110 may store member data 112, population data 114, claims data116, and/or pharmacy data 118.

The member data 112 includes information regarding the membersassociated with the benefit manager. Examples of the member data 112include name, address, telephone number, e-mail address, prescriptiondrug history, and the like. The member data 112 may include a plansponsor identifier that identifies the plan sponsor associated with themember and/or a member identifier that identifies the member to the plansponsor. The member data 112 may include a member identifier thatidentifies the plan sponsor associated with the patient and/or a patientidentifier that identifies the patient to the plan sponsor. The memberdata 112 may also include, by way of example, dispensation preferencessuch as type of label, type of cap, message preferences, languagepreferences, or the like. The member data 112 may be accessed by variousdevices in the pharmacy to obtain information utilized for fulfillmentand shipping of prescription orders.

In some embodiments, the member data 112 may include information forpersons who are patients of the pharmacy but are not members in abenefit plan being provided by the benefit manager. For example, thesepatients may obtain drug directly from the pharmacy, through a privatelabel service offered by the pharmacy, or otherwise. In general, the useof the terms member and patient may be used interchangeably herein.

Population data 114 includes information about persons residing invarious geographic areas. For example, the population data 114 mayinclude a postal code, e.g., ZIP code level, U.S. Census data(www.census.gov). The geographic area can also be defined by counties,city, town, or other political boundaries. The population data may alsoinclude pharmacy benefit manager device defined geographic areas, e.g.,associating member by the pharmacy that the member normally uses, e.g.,a pharmacy close to a member's work location instead of a home location.This pharmacy location information may be used in determining a pharmacymodel or determining pharmacy quality using the pharmacy model.

The claims data 116 includes information regarding pharmacy claimsadjudicated by the PBM under a drug benefit program provided by the PBMfor one, or more than one, clients. In general, the claims data 116 mayinclude client data (e.g., including an identification of the clientthat sponsors the drug benefit program under which the claim is made,company name, company address, contact name, contact telephone number,contact email address, and the like), an identification of the memberthat purchased the prescription drug giving rise to the claim, theprescription drug that was filled by the pharmacy (e.g., the nationaldrug code number), the dispensing date, generic indicator, GPI number,medication class, the cost of the prescription drug provided under thedrug benefit program, the copay/coinsurance amount, rebate information,and/or member eligibility. The claims data 116 may also include claimsadjudicated for healthcare related services other than prescriptionsfilled under a drug benefit program. Examples of other healthcarerelated services may include medical services (such as treatment,screening services, and laboratory services), dental related services,and vision care related services. Additional information may be includedin the various claims of the claims data 116. The claims data 116 mayalso be used in determining a pharmacy model or determining pharmacyquality using the pharmacy model.

The pharmacy data 118 may include information regarding pharmacies. Thepharmacy data 118 may include, by way of example, national provideridentifier information associated with the pharmacies, location dataregarding the location of the pharmacies, information data regarding thepharmacy hours and/or telephone number, pharmacy network associationdata defining the pharmacy network associations of which the pharmaciesare associated, and the like. The pharmacy data 118 may be used indetermining a pharmacy model or determining pharmacy quality using thepharmacy model.

While the system 100 in FIG. 1 is shown to include single devices 102,106, 108 multiple devices may be used. The devices 102, 106, 108 may bethe same type of device or may be different device types. When multipledevices are present, the multiple devices may be of the same device typeor may be a different device type. Moreover, system 100 shows a singlenetwork 104; however, multiple networks can be used. The multiplenetworks may communicate in series with each other to link the devices102, 106, 108 or in parallel to link the devices 102, 106, 108. Whendetermining a pharmacy model or determining pharmacy quality thesedevices 102, 106, 108 may perform the determination method either aloneor in combination(s).

FIG. 2 illustrates the user device 102, according to an exampleembodiment. The user device 102 may be used by a device operator toperform risk adjustment pharmacy modeling. The user device 102 may bedeployed in the system 100, or may otherwise be used.

The user device 102 may include a pharmacy modeling subsystem 202. Insome embodiments, the pharmacy modeling subsystem 202 may enable a riskadjustment predictive model to be generated and/or used.

FIG. 3 illustrates the benefit manager device 106, according to anexample embodiment. The benefit manager device 106 may be used by adevice operator to perform risk adjustment pharmacy modeling. Thebenefit manager device 106 may be deployed in the system 100, or mayotherwise be used.

The benefit manager device 106 may include the pharmacy modelingsubsystem 202. In some embodiments, the pharmacy modeling subsystem 202when used may provide server-side functionality to the user device 102.By way of example, the pharmacy modeling subsystem 202 may be deployedin both the user device 102 and the benefit manager device 106. The userdevice 102 may then perform some of the functionality while otherfunctionality is performed by the benefit manager device 106.

The pharmacy modeling subsystem 202 may be used to analyze pharmacyquality measures. In some embodiments, a method or study may be used asat least part of the analysis. Example methods are described below.

FIG. 4 shows a method 400 for using the methods and systems describedherein to evaluate a pharmacy or drug dispenser to determine a true riskand performance of the pharmacy. The present method uses large data setsto determine the performance of the pharmacy by using data available toa pharmacy organization, e.g., a pharmacy benefits manager, along withdata from other sources.

At block 402, data is gathered. The data can be gathered from varioussources including analyzed and aggregate pharmacy claims data, patientdemographic, and geographic data. This data can then be reformulated tobe at a pharmacy level. That is, the data is associated to an individualpharmacy or an insured company. The data is not identified to anindividual. The data can be from public databases or derived fromprivate and public databases. Data can include member data 112,population data 114, and claims data 116. This gathered data may be alldata that can be associated with a pharmacy, including patientdemographic data, patient socioeconomic data, pharmacy benefitattributes, pharmacy location, patient characteristics at the pharmacyand therapy classes.

At block 404, the gathered data is transformed into factors that relateto an individual pharmacy. Examples include demographic attributes ofpatients serviced at the pharmacy. A factor can be pharmacysocio-economic attributes of patients serviced at the pharmacy. A factorcan be pharmacy benefit design attributes of patients serviced at thepharmacy. A factor can be pharmacy location variables, e.g., U.S. censusregion, postal code, voting district, county, municipal area, city,state, congressional districts and the like. A factor can be therapyclass variables, percent of claims dispensed by the pharmacy for eachtherapy class included in the present analysis. A therapy class can be aspecialty therapy class, e.g., Inflammatory Conditions, MultipleSclerosis, Cancer, HIV, Growth Deficiency, Miscellaneous CNS Disorders,Respiratory Conditions, Anticoagulants, Transplant, PulmonaryHypertension and the like. A therapy class can be a traditional therapy,e.g., Diabetes, High Blood Cholesterol, High Blood Pressure/HeartDisease, Ulcer Disease, Asthma, Attention Disorders, Depression,Mental/Neurological Disorders, Pain, Infections and the like. Sometherapies can be included; some therapies can be excluded.

At block 406, the number of dimensions of the factors is reduced. Thefactors that are determined based on data relating to the pharmacy cannumber in the hundreds, if not thousands. Such a large number of factorscannot be efficiently analyzed. At block 406, the large number offactors (e.g., input variables) is reduced into a subset of factors thatcontain the most information about the model. This process reduces dataredundancy and keeps only the factors which contain strong signal, whichwill influence the calculated end value. An example of dimensionreduction is principal component analysis. Principal component analysisis a multivariate technique for examining relationships among severalquantitative variables. Principal component analysis detects linearrelationships in the data and reduces the number of variables inregression modeling. As input principal component analysis can use rawdata, a correlation matrix, a covariance matrix, or asum-of-squares-and-crossproducts (SSCP) matrix. Principal componentanalysis creates output data sets containing eigenvalues, eigenvectors,and standardized or unstandardized principal component scores. Principalcomponent analysis is a multivariate technique for examiningrelationships among several quantitative variables. Another example ofdimension reduction is factor analysis. Factor analysis is a statisticalmethod used to describe variability among observed, correlated variablesin terms of a potentially lower number of unobserved variables.

At block 408, factors are further reduced using a variable reductiontechnique. A variable reduction technique is used to select the mostrelevant factors for the final regression modeling. A k-fold crossvalidation option within the procedure can be used to arrive at the listof important variables. A variable reduction technique can effectselection in the framework of general linear models. A variety of modelselection methods can be used, e.g., GLMSELECT procedure from SAS, theLASSO method of Tibshirani (1996) and the related LAR method of Efron etal. (2004). The variable selection can customize the selection with awide variety of selection and stopping criteria, from traditional andcomputationally efficient significance-level-based criteria to morecomputationally intensive validation-based criteria. These can be set tobest operate on the gathered data and the pharmacy model factors.

After completing performance of the operations at blocks 406 and 408,the factors relating a pharmacy's quality, risk and/or performance arereduced by an order or magnitude or from thousands to ten to twentyfactors that contain data that will determine a true performance metricof the pharmacy for a therapy, overall performance, etc.

At block 410, a model for the pharmacy is selected. The selection of themodel is done using a statistical analysis, e.g., a STEPWISE regressionmethod. Examples of stepwise regression can include stepwise regressionincludes regression models in which the choice of predictive variablesis carried out by an automatic procedure, which can take the form of asequence of F-tests or t-tests, but other techniques are possible, suchas adjusted R-square, Akaike information criterion, Bayesian informationcriterion, Mallows's Cp, PRESS, or false discovery rate. The stepwiseregression can include forward selection, backward elimination orbidirectional elimination. The stepwise regression introduces onevariable at a time at every “step” and evaluates the model performanceat that step. This approach allows the method 400 to arrive at the mostoptimal algorithm for the model with variables that contain the mostamount of information. Note that the model selection is performed on thereduced factor set from block 408.

At block 412, a sensitivity analysis can be performed. The sensitivityanalysis allows the outlying factors to be analyzed to determine if theoutlying factors should be part of the model. Further, influential datapoints can be used to evaluate model robustness. Sensitivity analysiscan quantify the uncertainty in any model results, e.g., uncertaintyanalysis. The sensitivity analysis can further evaluate how much eachinput is contributing to the output uncertainty. Sensitivity analysiscan addresses these issues, performing the role of ordering byimportance the strength and relevance of the inputs in determining thevariation in the output. The sensitivity analysis can further accountfor computational expense in the pharmacy model.

At block 414, a final model is determined and the final model is used todetermine the pharmacy performance, pharmacy quality or pharmacy risk.This final model will use more than ten factors and less thantwenty-five factors. In an example, the factors for any given model arein the range of 16-20 factors. The final model includes the factors thataffect the pharmacy performance for the pharmacy therapy class for whichthe model is being developed. The factors can also be assigned weightswithin the model. For example, in the development of a genericdispensing model, the method 400 has identified that demographic andsocio-economic attributes of the members (patients) is a factor orfactors, along with pharmacy benefits and location of the pharmacy beingfactors. However, location of the pharmacy may be of lesser importanceto the model than some of the demographic and socio-economic factors,e.g., the percent of female patients at the pharmacy in the therapyclass or average age of patients. Accordingly, a higher weight is givento the demographic and socio-economic factor than to the pharmacylocation factor.

The method 400 can be executed on the subsystems 202 or the computingdevice of FIG. 6, which is described below. The method 400 can beexecuted to determine the statistical models using SAS 9.3 statisticalsoftware. Specifically, the SAS procedures PROC GLMSELECT may be usedfor dimensionality reduction and PROC REG with STEPWISE option may beused for model selection.

The method 400 takes a first set of factors, which can be quite large(e.g., in the hundreds or thousand factors), and reduces the first sectof factors through a series of iterations (e.g., blocks 406-412) toarrive at a final set of factors that influence the rating of thepharmacy for a particular therapy class. The method 400 removesredundant factors and factors that do not influence the end result ofthe pharmacy model.

The example method 400 may analyze two types of pharmacy qualitymeasures at an individual pharmacy. The first one is the GenericDispensing Rate (GDR), which is defined as the proportion of genericprescriptions divided by the total number of prescriptions. The secondmeasure that may be analyzed relates to medication adherence. These twopharmacy quality measures are illustrative of the present methods andsystems and the present disclosure is not limited to only GDR andadherence.

GDR is a pharmacy measure used in evaluating pharmacy benefit designs. Aprescription drug plan with higher GDR means that the prescription drugcosts are lower for members insured under the plan. Four separatemeasures may be analyzed (e.g., by the pharmacy modeling subsystem 202)at the pharmacy level. In some embodiments, these four measures mayprovide a good combination of cost and quality at a pharmacy, which aretwo factors in defining value of a service provided by the pharmacy.

The example method that may be performed in conjunction with thepharmacy modeling subsystem 202 may include a retrospectivecross-sectional method conducted using pharmacy claims data ofcommercially insured and Medicare patients. The method may involveanalyzing claims data from approximately 50,000 different retailpharmacies belonging to the largest pharmacies chains and independentpharmacies in the U.S. However, a different number of retail pharmaciesmay be selected. A method period, such as an annual basis, e.g., April2012-March 2013, may be selected.

An example method may use the data sourced from a comparatively largepharmacy benefits management company that provides pharmacy benefits forcommercially insured and Medicare patients. The de-identified data maycontain information about patients' claim history, enrollment data anddemographic information like age, gender and their geographic location(ZIP). De-identified is defined to include data that does not identify aperson individually. The data may also include information about retailpharmacies, such as pharmacy type, location, and affiliation to acorporate chain. This data may be merged with location information,e.g., ZIP level US Census data, to obtain socio-economic characteristicsof the study population. Other location information can be used in thepharmacy model. The data may also include more detailed data regardingsocio-economic characteristics that may be obtained or derived fromthird party databases and correlated to the pharmacy data.

The unit of analysis in an example method that may be performed inconjunction with the pharmacy modeling subsystem 202 may be a retailpharmacy. When the analysis is divided into two separate-analyses, thepopulation selection criteria for these analyses may be different.

For GDR example of the model identification method, all the retailpharmacies that had at least one pharmacy claim during the study periodmay be identified. In some embodiments, pharmacies that are not coded asretail pharmacy may be excluded. For example, the exclusion may includespecialty pharmacies, Long Term Care facilities, Veteran AdministrationUnits, Military Treatment Facilities and pharmacies within clinics. Itis envisioned that the present disclosure can be adapted to specialtypharmacies by a type of specialty pharmacy, where a type of specialtypharmacy is compared to a same type of specialty pharmacy. From thisset, all the pharmacies that had dispensed less than 1000 claims duringthe study period may be excluded. However, other numbers of claims suchas 100, 200, 500, 1500, 2000 or otherwise may be used.

For adherence, all patients who had at least two pharmacy claims for anyof the therapy classes of interest (e.g., Antidiabetics,Antihypertensives and Antihyperlipidemia) may be identified. Patientswho had a mail order claim or had filled their medications in any of thespecialty pharmacies, Long Term Care facilities, Veteran Admin Units,Military Treatment Facilities and pharmacies within clinics may then beexcluded from the method. From this list of patients and retailpharmacies, any pharmacy that had less than five patients for thetherapy class that was studied may be excluded. However, more or lesspatients may be used as the threshold exclusion.

A patient or member threshold can be applied in the creation of anypharmacy model or the application of the pharmacy model to a particularpharmacy. The threshold ensures that the there is sufficient data toperform the method 400 to create pharmacy model. The threshold is usedin the model as applied to a pharmacy to ensure that the model producesaccurate results with regard to a therapy class.

The method may use the residuals (or error terms) obtained from themultivariate statistical models developed for the pharmacy qualitymeasures to measure the ‘true’ performance of a pharmacy after adjustingfor the factors that influence the quality measure.

An example method can be represented as follows:

An expected value (E_(i)) of the quality measure obtained from themultivariate adjusted statistical model for each pharmacy (i). Theexpected value E_(i) can be calculated using a model determined from themethod 400. Hence the expect value is based on the limited number offactors in the model and not all factors related to the pharmacy. Theexpected value is based on twenty or fewer factors that are part of aparticular model. The model may not be limited to a particular pharmacyand may be used for other pharmacies. The model may, however, be limitedto a particular quality measure, e.g., GDR or adherence to a particulardrug regimen. An actual value (A_(i)) of the quality measure as computedfrom the claims information for each pharmacy (i).Residual of the quality measure at a pharmacy=A _(i) −E _(i).

For example, consider a pharmacy A with a GDR of 63%, which means 63% ofthe prescriptions filled in that pharmacy are generics. After adjustingfor factors such as member demographics, plan design, geography and soon, if a determination is made that expected GDR (e.g., using the modelfrom the method 400) at that pharmacy is 60%, then the residual is apositive 3%. The positive 3% residual reflects that pharmacy A isperforming better than expected on GDR measure. A similar approach maybe used for adherence measures of pharmacy performance as well. Hence, apositive residual is an indication of good performance from a pharmacyand conversely a negative residual means that the pharmacy is notperforming to the expectation.

The analysis performed by the method may be split into two sub-analysesin order to study GDR and medication adherence separately. In the firstanalysis, a multivariate regression model for GDR may be developed,which adjusted for factors that could be beyond the control of a retailpharmacy. Some of these factors include patient demographics, pharmacybenefits or plan factors and geographic location of the pharmacy, whichcan be selected using the method 400.

Similarly, the second analysis examined medication adherence at apharmacy controlling for factors that are beyond the control of a retailpharmacy. Since three separate adherence measures are being analyzed,each of the measure may be modeled individually using multivariatestatistical model. Proportion of Days Covered (PDC), may be used as themeasure of patients' adherence to a medication. A patient with PDC ≧0.8for the therapy class analyzed may be considered adherent.

The pharmacy modeling subsystem 202 may be used as part of thestatistical model development. Pharmacy modeling subsystem 202 may usethe method 400 to establish the model to determine pharmacy performance.

The model may be developed separately for the GDR portion relative tomedication adherence portion of the method.

Generic Dispensing Rate

Generic Dispensing Rate (GDR) at a pharmacy is generally defined as theproportion of number generic drugs dispensed over total number drugsdispensed during the study period. This ratio may be bounded between 0and 1. The method may compute this metric for a number of pharmacies inthe method (e.g., all pharmacies being studied) along with various otherpharmacy, patient, benefit and geographic attributes. The GDR metric maythen be used as a dependent variable with other factors as independentfactors. A multivariate linear regression model may be developed using⅔^(rd) of the data as training sample and the remaining ⅓^(rd) as thevalidation sample. However, other proportions may also be used.

Table 1 shows a list of variable factors that can be used in a GDRmodel. The domain indicates an area where the factor can be grouped

TABLE 1 Variables in Generic Dispensing Ratio (GDR) model DomainVariable Demographic & Average age of patients Socioeconomic % Femalepatients attributes Average ZIP level household income of patientsPharmacy benefit Average Copay for brand drugs dispensed designattributes Average Copay for generic drugs dispensed Proportion claimswith Prior Authorization Pharmacy location US Census Region Therapyclass ADHD variables, % claims Allergy dispensed Antianxiety AntibioticsAntidepressants Antifungals Antivirals Asthma & COPD Cough & ColdDermatological Diabetes Ear, Nose & Throat Hormone ReplacementHypertension Lipid lowering Ophthalmological Urological

Medication Adherence

Medication adherence for each pharmacy may be modeled at the therapylevel. Hence, separate models may be developed for Antidiabetics,Antihypertensives and Antihyperlipidemia. To model these conditions, aproportion of patients who are adherent to their medication for eachpharmacy may be computed.

The ratio may be calculated as below (an example for Antidiabetics)

${{Proportion}\mspace{14mu}{of}\mspace{14mu}{adherent}\mspace{14mu}{patients}\mspace{14mu}{at}\mspace{14mu}{pharmacy}} = \frac{\begin{matrix}{{Number}\mspace{14mu}{of}\mspace{14mu}{patients}\mspace{14mu}{taking}\mspace{14mu}{oral}\mspace{14mu}{diabetes}} \\{{{medication}\mspace{14mu}{and}\mspace{14mu}{PDC}} \geq 0.8}\end{matrix}}{{Number}\mspace{14mu}{of}\mspace{14mu}{patients}\mspace{14mu}{taking}\mspace{14mu}{oral}\mspace{14mu}{diabetes}\mspace{14mu}{medication}}$

The metric may be computed for every retail pharmacy in the method alongwith various other pharmacy, patient, benefit and geographic attributes.The calculated proportion of adherent patients at pharmacy may then beused as a dependent variable with other factors as independent factors.A multivariate linear regression model was developed using ⅔^(rd) of thedata as training sample and the reaming ⅓^(rd) as the validation sample.

Table 2 shows variable factors that can be used in a model to computeadherence.

TABLE 2 Final list of variables in Adherence models Domain VariableDemographic & Average age of patients Socioeconomic % Female patientsattributes Average ZIP level household income of patients Pharmacybenefit Average Copay for drugs dispensed design attributes for thetherapy class studied Average days supply per Rx Pharmacy location USCensus Region Patient characteristics Average Chronic Disease Score(CDS) at the pharmacy for patients at the pharmacy Percent of patientsnew to therapy Therapy class Antibiotics variables, % claimsAntidepressants dispensed Antifungals Lipid Lowering Narcotics & PainRelief Thyroid Ulcer and Heartburn

After development of the statistical models, calculation of the expectedperformance measures for each pharmacy in a selected sample may beperformed. Residual for each quality measure may then be calculated asthe difference between actual value of the measure minus the expectedvalue of the measure.

Results for the Medicare Model

Generic Dispensing Rate

In an example embodiment, the final sample size for the Medicare GDRmodel may be 36,281 pharmacies. The results from the statistical modelare provided in Table 3.

TABLE 3 Results from the Medicare GDR model (Dependent variable = GDR)Estimate Pr > Model Covariates Parameters |t| Average patient copay forall the −0.005 <.0001 generic medications filled at the pharmacy AverageZIP level household income −0.00000023 <.0001 patients filling at thepharmacy Proportion of females filling −0.00026 <.0001 at the pharmacyProportion of claims dispensed 0.002 <.0001 for Antianxiety Proportionof claims dispensed 0.004 <.0001 for Antibiotics Proportion of claimsdispensed 0.005 <.0001 for Antidepressants Proportion of claimsdispensed 0.007 <.0001 for Antifungals Proportion of claims dispensed−0.006 <.0001 for Antivirals Proportion of claims dispensed −0.007<.0001 for Asthma and COPD Proportion of claims dispensed −0.005 <.0001for Dermatological Proportion of claims dispensed −0.005 <.0001 for EarNose & Throat Proportion of claims dispensed 0.004 <.0001 forHypertension Proportion of claims dispensed −0.008 <.0001 forMiscellaneous agents Proportion of claims dispensed 0.002 <.0001 forNarcotic Pain Relief Proportion of claims dispensed −0.005 <.0001 forOphthalmological Proportion of claims dispensed 0.006 <.0001 forHormones Proportion of claims dispensed −0.005 <.0001 for Select BiotechProportion of claims dispensed 0.003 <.0001 for Thyroid Proportion ofclaims dispensed 0.001 <.0001 for Ulcer and Heartburn Percent of PriorAuthorizations −0.009 <.0001 (PA) at the pharmacy pharmacy locationstate is AK −0.046 <.0001 pharmacy location state is AZ 0.008 <.0001pharmacy location state is CT −0.024 <.0001 pharmacy location state isDC −0.021 <.0001 pharmacy location state is DE −0.022 <.0001 pharmacylocation state is GA −0.006 <.0001 pharmacy location state is IA 0.017<.0001 pharmacy location state is IN −0.011 <.0001 pharmacy locationstate is KY −0.008 <.0001 pharmacy location state is LA −0.045 <.0001pharmacy location state is MA 0.030 <.0001 pharmacy location state is MD−0.004 0.0018 pharmacy location state is MI 0.026 <.0001 pharmacylocation state is MN 0.029 <.0001 pharmacy location state is NC −0.011<.0001 pharmacy location state is NJ −0.061 <.0001 pharmacy locationstate is NM 0.013 <.0001 pharmacy location state is NV 0.010 <.0001pharmacy location state is NY −0.020 <.0001 pharmacy location state isOH 0.006 <.0001 pharmacy location state is OR 0.014 <.0001 pharmacylocation state is PA −0.005 <.0001 pharmacy location state is SC −0.021<.0001 pharmacy location state is TN −0.005 <.0001 pharmacy locationstate is TX −0.022 <.0001 pharmacy location state is UT 0.010 <.0001pharmacy location state is WA 0.012 <.0001 pharmacy location state is WI0.019 <.0001 pharmacy location state is WV −0.019 <.0001

Medication Adherence

In an example embodiment, the results from the statistical modelsdeveloped for medication adherence measures are provided in Table 4.

TABLE 4 Results from the Medicare medication adherence models (Dependentvariable = % of Patients with PDC ≧ 80%) Parameter Estimates LipidCovariates Diabetes Hypertension Lowering Average age of patientsserviced at the pharmacy 0.26 0.64 0.43 Average ZIP level householdincome patients 0.0000648 0.000112 0.000153 filling at the pharmacyAverage weighted Chronic Disease Score (CDS) at −0.04 0.07 0.05 thepharmacy Number of prescriptions per patient for non- 0.25 — —antihypertensive medications Proportion of claims dispensed forAntibiotics −0.75 — −0.54 Proportion of claims dispensed forAnticoagulants −0.28 — — and Antiplatelet Proportion of claims dispensedfor Antidepressants — 0.76 0.63 Proportion of claims dispensed forAntifungals −1.12 — — Proportion of claims dispensed for Lipid lowering0.17 0.23 — Proportion of claims dispensed for Narcotic Pain −0.59 −0.34−0.34 Relief Proportion of claims dispensed for Thyroid — 0.57 0.45Proportion of claims dispensed for Ulcer and — — 0.3 HeartburnProportion of females filling at the pharmacy −0.04 −0.08 −0.1 Percentof patients who are new to therapy for — — −0.06 study medicationPharmacy in an urban area −2.38 −2.33 −2.95 Pharmacy location state isAL — — −1.99 Pharmacy location state is AR — — −1.65 Pharmacy locationstate is CA −1.51 −3.82 −3.09 Pharmacy location state is DC −10.08 —−10.16 Pharmacy location state is FL — −2.47 −3.32 Pharmacy locationstate is GA −2.53 −4.84 −5.49 Pharmacy location state is HI — — −4.51Pharmacy location state is IA 4.6 — 3.78 Pharmacy location state is LA —−3.6 −2.94 Pharmacy location state is MD — — −2.5 Pharmacy locationstate is ME — — 5.89 Pharmacy location state is MI — 2.9 2.51 Pharmacylocation state is MN — 4.78 5.13 Pharmacy location state is MO — — 1.3Pharmacy location state is MS — −3.56 −4.29 Pharmacy location state isMT — — 6.25 Pharmacy location state is NC — −1.98 −4.15 Pharmacylocation state is ND — — 7.3 Pharmacy location state is NJ −1.93 — −2.82Pharmacy location state is NM — — −3.67 Pharmacy location state is NV−2.04 — −2.67 Pharmacy location state is NY — — −0.83 Pharmacy locationstate is PA 2.2 — — Pharmacy location state is SC — −3.34 −4.69 Pharmacylocation state is TN — — −2.11 Pharmacy location state is TX −2.07 −4.21−3.81 Pharmacy location state is UT — −5.81 −2.9 Pharmacy location stateis VA — — −1.99 Pharmacy location state is VT — — 7.77 Pharmacy locationstate is WA — — 1.4 Pharmacy location state is WI 4.95 4.49 4.33

The results from both GDR model and medication adherence models in theexample show that the factors like demographics, copay information,geography and the therapeutic mix at a pharmacy is statisticallycorrelated to the pharmacy quality measures via the model as determinedfor a therapy class. Hence, these factors may be considered and adjustedfor by the pharmacy modeling subsystem 202 while calculating theperformance measures. These factors can be selected using the method400.

Commercial Population

A similar methodology (as described in the above sections) was used todevelop the statistical models for the Commercial population(Employer-sponsored health insurance plans). The tables below providethe results of examples of these analyses.

Results for the Commercial Model

Generic Dispensing Rate

The results from the GDR model show that the factors like demographic,socioeconomic, benefit design, geographic and therapy class variables ata pharmacy are statistically correlated to the pharmacy quality measure(GDR). These factors are to a large extent not under the control of thepharmacies and hence the pharmacies cannot influence their performanceby controlling these factors. The final sample size for the GDR modelwas 47,509 pharmacies. The results from the example statistical modelare provided in Table 5.

TABLE 5 Results from the Commercial GDR model (Dependent variable = GDR,n = 47,509) Parameter Model Covariates estimate P-Value Demographic &Socioeconomic attributes of patients serviced at the pharmacy Averageage of patients −0.00044842 P < 0.001 % Female patients 0.00040943 P <0.001 Average ZIP level household income −0.000000695141 P < 0.001 ofpatients Pharmacy benefit design attributes of patients serviced at thepharmacy Average Copay for brand drugs dispensed 0.0003817 P < 0.001Average Copay for generic drugs dispensed −0.00731 P < 0.001 % PriorAuthorization claims −0.00284 P < 10.001 Therapy class variables, %claims dispensed ADHD −0.00263 P < 0.001 Allergy 0.00068121 P < 0.001Antianxiety 0.00088845 P < 0.001 Antibiotics 0.00292 P < 0.001Antidepressants 0.00242 P < 0.001 Antifungals 0.00452 P < 0.001Antivirals −0.00702 P < 0.001 Asthma & COPD -0.00612 P < 0.001 Cough &Cold 0.00188 P < 0.001 Dermatological −0.00301 P < 0.001 Diabetes−0.0036 P < 0.001 Ear, Nose & Throat −0.00564 P < 0.001 HormoneReplacement −0.00971 P < 0.001 Hypertension 0.00245 P < 0.001 Lipidlowering −0.0028 P < 0.001 Miscellaneous Agents −0.00841 P < 0.001Ophthalmological −0.00453 P < 0.001 Select Biotech −0.00842 P < 0.001Urological −0.00585 P < 0.001 Pharmacy location variables, US States CT−0.0298 P < 0.001 DC −0.03553 P < 0.001 DE −0.04055 P < 0.001 GA−0.00922 P < 0.001 IA 0.01312 P < 0.001 ID 0.02049 P < 0.001 IN −0.0179P < 0.001 LA −0.02333 P < 0.001 MA 0.05442 P < 0.001 MD −0.01635 P <0.001 MI 0.02425 P < 0.001 MN 0.04192 P < 0.001 MS −0.02101 P < 0.001 NC0.00348 P < 0.001 NH 0.02749 P < 0.001 NJ −0.08862 P < 0.001 NM 0.02618P < 0.001 NY −0.01218 P < 0.001 OH −0.00605 P < 0.001 OK −0.00997 P <0.001 OR 0.03882 P < 0.001 PA −0.01851 P < 0.001 PR −0.11549 P < 0.001SC −0.01251 P < 0.001 TX −0.02548 P < 0.001 UT 0.02104 P < 0.001 VA−0.00619 P < 0.001 WA 0.03234 P < 0.001 WI 0.0161 P < 0.001 WV −0.02349P < 0.001

Medication Adherence

The results from the example statistical models developed for medicationadherence measures are provided in Table 6. Similar to the GDR model,the medication adherence model results show that factors likedemographic, socioeconomic, patients' disease severity, benefit design,geographic and therapy class variables at a pharmacy are statisticallycorrelated to the quality measure modeled (% of patients' adherent totheir medications). For the medication adherence measures, it can beseen that the disease severity indicator (CDS), geographic location ofthe pharmacy (region) and the concomitant utilization of drugs in otherselect therapy classes for patients studied at the pharmacy have astatistically significant correlation with the quality measure. When thepharmacy modeling subsystem 202 measures quality at a pharmacy, thepharmacy modeling subsystem 202 may adjust for the factors. Theadjustment can be made using the method 400 or be made duringcalculation when a factor is determined to not have an effect on theoutcome or is redundant to another factor.

TABLE 6 Results from the Commercial medication adherence models(Dependent variable = % of adherent patients) AntidiabeticsAntihypertensives Antihyperlipidemics Model Covariates (n = 31,974) (n =29,415) (n = 37,872) Demographic & Socioeconomic attributes of patientsserviced at the pharmacy Average age of patients 0.7648* 0.74* 0.89* %Female patients −0.07* −0.20* −0.16* Average ZIP level 0.000116*0.000093* 0.00015* household income of patients Pharmacy benefit designattributes of patients serviced at the pharmacy Average Copay for drugs— −0.105* −0.08* dispensed within the therapy class studied Averagedays' supply per 2.09* 2.44* 2.67* Rx Patient characteristics at thepharmacy Average Chronic Disease 0.16* 0.09* 0.06* Score (CDS¹) forpatients at the pharmacy % patients new to therapy −0.04* −0.088* −0.08*for the therapy class modeled Therapy class variables, % claimsdispensed for patients studied for the therapy class modeled Antibiotics−1.10* — −0.76* Antidepressants — 0.79* 0.63* Antifungals −0.96* −1.85*— Lipid Lowering 0.166* 0.32* — Narcotics & Pain Relief −0.33* −0.15*−0.15* Thyroid — 0.62* 0.37* Ulcer and Heartburn — 0.62* 0.43* Pharmacylocation variables, Urban status /US States Pharmacy located in an−3.74* −2.38* −3.24* urban area indicator² AL — −1.95* −1.84* AZ −3.01*−4.23* — CA −3.74* −4.59* −3.22* CO −5.10* −5.63* — DE — — 7.01* FL−3.83* −5.79* −3.55* GA −4.20* −5.99* −3.98* IL — — 2.15* KS 3.08* —3.35* KY 13.86* 3.59* 10.15* LA — −4.03* −4.50* MI 4.01* 3.49* 3.07* MN6.88* 2.44* 5.35* MO — — 2.45* MS — −4.52* −4.28* NC — −1.86* −1.10* ND12.83* — 10.12* NM — −3.98* — NV −6.25* −6.06* −4.16* OH — −1.79* — OK4.48* — 4.44* PA 4.26* 2.13* 4.52* SC — −2.21* −2.17* TN — −2.94* −1.56*TX −6.09* −6.50* −5.13* UT −4.33* −7.78* −3.02* VA 2.66* — — VT 11.13* —5.45* WA — −1.65* 1.60* WI 4.79* 3.95* 5.70* WV 5.75* — 3.11*

Chronic Disease Score is a disease severity or disease burden index.

Core Based Statistical Area (CBSA) provided by US census bureau was usedto determine if the pharmacy is located in an urban area.

* Estimates are statistically significant at P <0.001.

FIG. 5 is an example display, according to an example embodiment. Thedisplay includes a chart 500 that reflects a comparison of the patientpopulations associated with two pharmacies.

The risk adjustment pharmacy model may be use to determine the standardto which a pharmacy may be held. For example, without associatedpharmacy data, the pharmacy may be held to a national adherence average(e.g., that 69.5% of the patients should be adherent or adherence rate).

Analysis may be performed using the pharmacy modeling subsystem 202 todetermine 11% of the patients of Pharmacy A are on pain medications,while 2% of the patients of Pharmacy B are on pain medications. In someembodiments, the risk adjustment pharmacy model may reflect that themore patients that a pharmacy has that are taking pain medication theless likely that the patients of the pharmacy are to be adherent. Basedon an adjustment determined by the pharmacy model, Pharmacy B shouldactually be performing at a 71.7% adherence rate and Pharmacy A shouldbe performing at 66.5% adherence rate.

Analysis may be performed using the pharmacy modeling subsystem 202 todetermine the average age of the patient population of Pharmacy A andPharmacy B. The analysis may determine that Pharmacy A has a youngerpopulation and that younger people are less adherent (e.g., toprescription drugs). Based on an adjustment determined by the riskadjustment pharmacy model, Pharmacy B should actually be performing at a73.6% adherence rate and Pharmacy A should be performing at 65.4%adherence rate.

Analysis may be performed using the pharmacy modeling subsystem 202 todetermine the disease severity of the patient population of Pharmacy Aand Pharmacy B. In some embodiments, the risk adjustment pharmacy modelmay reflect that patients who are sicker take their medications moreregularly because they take their conditions more seriously. Based on anadjustment determined by the risk adjustment pharmacy model, Pharmacy Bshould actually be performing at a 73.6% adherence rate and Pharmacy Ashould be performing at 64.7% adherence rate.

Analysis may be performed using the pharmacy modeling subsystem 202 todetermine the average household income of the patient population ofPharmacy A and Pharmacy B. In some embodiments, the risk adjustmentpharmacy model may reflect that people of lower income are less likelyto be adherent. As reflected, the patient population of Pharmacy A has asignificantly lower income than the patient population of Pharmacy B.Based on an adjustment determined by the risk adjustment pharmacy model,Pharmacy B should actually be performing at a 74.4% adherence rate andPharmacy A should be performing at 64.2% adherence rate.

Analysis may be performed using the pharmacy modeling subsystem 202 todetermine the prescription drug utilization of concomitant therapies ofthe patient population of Pharmacy A and Pharmacy B. Based on anadjustment determined by the risk adjustment pharmacy model, Pharmacy Bshould actually be performing at a 78.4% adherence rate and Pharmacy Ashould be performing at 67.8% adherence rate.

The various adherence rates may be combined to reflect an 80.1%adherence rate expectation for Pharmacy B versus the 66.7% expectationfor Pharmacy A.

FIG. 6 shows a block diagram of a machine in the example form of acomputer system 600 within which a set of instructions may be executedcausing the machine to perform any one or more of the methods,processes, operations, or methodologies discussed herein. The userdevice 102, the benefit management device 106, and/or the pharmacydevice 108 may include the functionality of the one or more computersystems 600.

In an example embodiment, the machine operates as a standalone device ormay be connected (e.g., networked) to other machines. In a networkeddeployment, the machine may operate in the capacity of a server or aclient machine in server-client network environment, or as a peermachine in a peer-to-peer (or distributed) network environment. Themachine may be a server computer, a client computer, a personal computer(PC), a tablet PC, a gaming device, a set-top box (STB), a PersonalDigital Assistant (PDA), a cellular telephone, a web appliance, anetwork router, switch or bridge, or any machine capable of executing aset of instructions (sequential or otherwise) that specify actions to betaken by that machine. Further, while only a single machine isillustrated, the term “machine” shall also be taken to include anycollection of machines that individually or jointly execute a set (ormultiple sets) of instructions to perform any one or more of themethodologies discussed herein.

The example computer system 600 includes a processor 602 (e.g., acentral processing unit (CPU) a graphics processing unit (GPU) or both),a main memory 604 and a static memory 606, which communicate with eachother via a bus 608. The computer system 600 further includes a videodisplay unit 610 (e.g., a liquid crystal display (LCD) or a cathode raytube (CRT)). The computer system 600 also includes an alphanumeric inputdevice 612 (e.g., a keyboard), a cursor control device 614 (e.g., amouse), a drive unit 616, a signal generation device 618 (e.g., aspeaker) and a network interface device 620.

The drive unit 616 includes a computer-readable medium 622 on which isstored one or more sets of instructions (e.g., software 624) embodyingany one or more of the methodologies or functions described herein. Thesoftware 624 may also reside, completely or at least partially, withinthe main memory 604 and/or within the processor 602 during executionthereof by the computer system 600, the main memory 604 and theprocessor 602 also constituting computer-readable media.

The software 624 may further be transmitted or received over a network626 via the network interface device 620.

While the computer-readable medium 622 is shown in an example embodimentto be a single medium, the term “computer-readable medium” should betaken to include a single medium or multiple media (e.g., a centralizedor distributed database, and/or associated caches and servers) thatstore the one or more sets of instructions. The term “computer-readablemedium” shall also be taken to include any medium that is capable ofstoring or encoding a set of instructions for execution by the machineand that cause the machine to perform any one or more of themethodologies of the present invention. The term “computer-readablemedium” shall accordingly be taken to include, but not be limited to,solid-state memories, and optical media, and magnetic media. In someembodiments, the computer-readable medium is a non-transitorycomputer-readable medium.

Generic Dispensing Rate is described herein as an example of qualitypharmacy practices. It will be understood that Generic Dispensing Rateis one example of quality of a pharmacy as it relates to generic drugutilization by a pharmacy. Other models may be used with relation togeneric drugs and quality of care of the pharmacy.

A described herein the present processes can be performed on a benefitmanager device. The present disclosure is not limited to benefit managerdevice and includes other devices that have access to the data, e.g., agovernment oversight device or industry wide device. Such a device willinclude circuitry that when executing any of the method steps describedherein become a dedicated machine executing the present method. It willbe understood that such a machine may be adaptable to other methods thatmay or may not relate to determining pharmacy performance or pharmacyrisk.

Measures of pharmacy quality or risk can be defined and measured withsome accuracy using pharmacy claims data, such a measure does not adjustfor factors that are beyond the control of the pharmacies. The factorscan include demographics of patients serviced, plan design factors,prior authorizations and such. Other factors that may be statisticallysignificant for some models in evaluating pharmacy performance or riskinclude age, gender, geography and patient copay—these can bestatistically significant effect on the patient's adherence to amedication. These factors also have an effect on a patient's behaviortowards generic drug utilization. Hence, it is important to develop apharmacy quality measure that control for these factors. The presentlydescribed systems and methods provide a methodology to empiricallyadjust the pharmacy quality measure that adjusts the influence offactors that are beyond the control of individual pharmacies.

The systems and methods described herein can relate particularperformance metrics on a pharmacy level to identify a more accurateperformance profile of a pharmacy, which can be used to compare apharmacy's performance with the performance of other pharmacies usingthe improved rating. This improved rating then can be used asbenchmarking data for future performance evaluations. Additionally,training and advise can be based on the improved rating to improvepharmacy performance. Any improvement action plan for a pharmacy is thentargeted and customized for that particular pharmacy.

The term “based on” or using, as used herein, reflects an open-endedterm that can reflect others elements beyond those explicitly recited.

Certain systems, apparatus, applications or processes are describedherein as including a number of modules. A module may be a unit ofdistinct functionality that may be presented in software, hardware, orcombinations thereof. When the functionality of a module is performed inany part through software, the module includes a computer-readablemedium. The modules may be regarded as being communicatively coupled.

The inventive subject matter may be represented in a variety ofdifferent embodiments of which there are many possible permutations.

Thus, methods and systems for pharmacy modeling have been described.Although embodiments of the present invention have been described withreference to specific example embodiments, it will be evident thatvarious modifications and changes may be made to these embodimentswithout departing from the broader spirit and scope of the embodimentsof the invention. Accordingly, the specification and drawings are to beregarded in an illustrative rather than a restrictive sense.

The methods described herein do not have to be executed in the orderdescribed, or in any particular order. Moreover, various activitiesdescribed with respect to the methods identified herein can be executedin serial or parallel fashion. Although “End” blocks are shown in theflowcharts, the methods may be performed continuously.

The Abstract of the Disclosure is provided to comply with 37 C.F.R.§1.72(b), requiring an abstract that will allow the reader to quicklyascertain the nature of the technical disclosure. It is submitted withthe understanding that it will not be used to interpret or limit thescope or meaning of the claims. In addition, in the foregoing DetailedDescription, it can be seen that various features are grouped togetherin a single embodiment for the purpose of streamlining the disclosure.This method of disclosure is not to be interpreted as reflecting anintention that the claimed embodiments require more features than areexpressly recited in each claim. Rather, as the following claimsreflect, inventive subject matter may lie in less than all features of asingle disclosed embodiment. Thus, the following claims are herebyincorporated into the Detailed Description, with each claim standing onits own as a separate embodiment.

What is claimed is:
 1. A method comprising: accessing member data,claims data, and population data; creating a pharmacy predictive modelbased on the member data, the claims data, and the population data;determining a calculated adherence percentage for a therapy class at apharmacy; calculating an expected adherence percentage for the therapyclass at the pharmacy using the pharmacy predictive model; and comparingthe calculated adherence percentage for the therapy class at thepharmacy to the expected adherence percentage for the therapy class atthe pharmacy.
 2. The method of claim 1, wherein the therapy classincludes at least one of the therapy class for antibiotics,antidepressants, antifungals, lipid lowering, narcotics & pain relief,thyroid, and ulcer and heartburn.
 3. The method of claim 1, whereincreating the pharmacy predictive model includes relating the member datato a particular pharmacy for use in creating the pharmacy model, whereinthe member data includes demographic attributes of the member.
 4. Themethod of claim 3, wherein creating the pharmacy predictive modelincludes compiling data based on location of the particular pharmacy. 5.The method of claim 3, wherein creating the pharmacy predictive modelincludes claims data relating to copay for drugs dispensed in thetherapy class.
 6. The method of claim 1, wherein creating the pharmacypredictive model includes compiling a first set of factors to possiblyrelate to adherence at the pharmacy, reducing the first set of factorsto a second set of factors that reflect only factors that influencepharmacy performance for adherence in the therapy class and includingthe second set of factors in the pharmacy predictive model.
 7. Themethod of claim 6, wherein creating the pharmacy predictive modelincludes assigning a weight to each of the factors in the second set offactors.
 8. The method of claim 6, wherein the second set of factorsincludes an average days supply per prescription factor.
 9. The methodof claim 6, wherein the second set of factors includes an averagechronic disease score for patients at a particular pharmacy factor and amember new to the therapy class factor.
 10. The method of claim 1,wherein creating the pharmacy predictive model includes excluding anypharmacy that fails to reach a minimum threshold of members.
 11. Themethod of claim 1, wherein determining a calculated adherence percentageincludes members who have at least two pharmacy claims in the therapyclass.
 12. A method comprising: accessing member data, claims data, andpopulation data; determining a calculated generic dispensing ratio for atherapy class at a pharmacy; calculating an expected generic dispensingratio for the therapy class at the pharmacy using a pharmacy predictivemodel; and comparing the calculated generic dispensing ratio for thetherapy class at the pharmacy to the expected generic dispensing ratiopercentage for the therapy class at the pharmacy.
 13. The method ofclaim 12, wherein the member data include average age of patients at thepharmacy and a level of income in an area at which the pharmacy islocated, and wherein determining includes using pharmacy location todetermine the calculated generic dispensing ratio and whereincalculating the expected generic dispensing ratio includes usingpharmacy location to calculate the expected generic dispensing ratio.14. The method of claim 13, wherein the pharmacy predictive modelincludes a variable of a ratio of the therapy class versus activity atthe pharmacy.
 15. The method of claim 14, wherein the therapy class isone of includes a variable of ADHD, Allergy, Antianxiety, Antibiotics,Antidepressants, Antifungals, Antivirals, Asthma & COPD, Cough & Cold,Dermatological, Diabetes, Ear, Nose & Throat, Hormone Replacement,Hypertension, Lipid lowering, Ophthalmological and Urological.
 16. Themethod of claim 12, wherein creating the generic dispensing ratiopredictive model includes compiling a first set of factors to possiblyrelate to generic dispensing ratio at the pharmacy, reducing the firstset of factors to a second set of factors that reflect only factors thatinfluence pharmacy performance for generic dispensing ratio in thetherapy class and including the second set of factors in the pharmacypredictive model, and wherein the second set of factors includes anaverage days supply per prescription Rx proportion of claims with priorauthorization factor.
 17. The method of claim 12, wherein creating thepharmacy predictive model includes compiling a first set of factors topossibly relate to generic dispensing ratio at the pharmacy, reducingthe first set of factors to a second set of factors that reflect onlyfactors that influence pharmacy performance for the generic dispensingratio and including the second set of factors in the risk adjustedpharmacy predictive model.
 18. The method of claim 12, wherein creatingthe pharmacy predictive model includes excluding any pharmacy that failsto reach a minimum threshold of members.